Anti-Money Laundering: Bridging Regulatory Compliance and Data Integrity
by Balaji Yellavalli
I got many interesting responses to my previous blog stressing the role of technology “engines” in meeting emerging regulations related to Anti-Money Laundering (AML) and illustrating the collateral benefits of such moves on customer experience.
I envision two critical “calls to action” especially in the context of regulations such as AML, Know Your Customer (KYC) and others that keep popping up every time a new and perverse way to engage in criminal activity is uncovered.
One, financial institutions need a strong “Unified Compliance Solution” (UCS) approach to integrate seemingly disparate compliance-monitoring activities across global lines of business. A UCS approach not only helps in linking apparently unconnected transactions to establish a potentially suspicious pattern and file a “Suspicious Activity Report “(SAR) with the regulators. It reduces time for investigation and focuses staff energies in going after the “bad guys” as opposed to disrupting genuine day-to-day transactions. A UCS also helps in integrating customer data which could help in gaining better insights into customer behavior, for improving customer experience and ultimately, customer satisfaction.
Infosys’ experience with leading financial institutions has borne out the fact that while Regulatory Compliance may be the impetus for a UCS, it is not the end game. A well-designed UCS can be leveraged for competitive advantage and ultimately, for winning in the turns, based on superior customer insight!
I know a top Wall Street Firm that consistently outperforms the competition by combining regulatory compliance initiatives with customer data integration. It is in an enviable position today because of investing ahead of time in aligning and unifying disparate silos of organizational information across their global operations.
The second call to action: it is assumed, when we discuss such topics, that the quality of transaction data generated by a firm’s systems is clean and reusable for further analysis. Unfortunately, it is not always the case.
As the complexity of transactions, coupled with their global spread increases, so does the scope for errors in collating and reporting. Independent studies by leading analysts have estimated that the business losses due to poor transaction data quality are in the order of US$1 billion to US$ 1.5 billion on an annual basis. It is ironical that financial institutions are actually losing money due to poor maintenance of data and information within their organization, far from making money from information! Hence, it is important to install strong data quality systems as an intermediate step before building the UCS engine.